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121 Publications


2020 | Conference Paper | IST-REx-ID: 15074 | OA
Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Brief Announcement: Efficient Load-Balancing through Distributed Token Dropping.” In 34th International Symposium on Distributed Computing, Vol. 179. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.DISC.2020.40.
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.” In 47th International Colloquium on Automata, Languages, and Programming, Vol. 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.ICALP.2020.7.
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 15086 | OA
Faghri, Fartash , Iman Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel Roy, and Ali Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2019 | Journal Article | IST-REx-ID: 6759 | OA
Jelínek, Vít, and Martin Töpfer. “On Grounded L-Graphs and Their Relatives.” Electronic Journal of Combinatorics. Electronic Journal of Combinatorics, 2019. https://doi.org/10.37236/8096.
[Published Version] View | Files available | DOI | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6931 | OA
Nowak, Thomas, and Joel Rybicki. “Byzantine Approximate Agreement on Graphs.” In 33rd International Symposium on Distributed Computing, 146:29:1--29:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. https://doi.org/10.4230/LIPICS.DISC.2019.29.
[Published Version] View | Files available | DOI | arXiv
 

2019 | Conference Paper | IST-REx-ID: 5947 | OA
Chatterjee, Bapi, Sathya Peri, Muktikanta Sa, and Nandini Singhal. “A Simple and Practical Concurrent Non-Blocking Unbounded Graph with Linearizable Reachability Queries.” In ACM International Conference Proceeding Series, 168–77. ACM, 2019. https://doi.org/10.1145/3288599.3288617.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Poster | IST-REx-ID: 6485
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019. https://doi.org/10.1145/3293883.3297000.
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2019 | Journal Article | IST-REx-ID: 6936 | OA
Ovaskainen, Otso, Joel Rybicki, and Nerea Abrego. “What Can Observational Data Reveal about Metacommunity Processes?” Ecography. Wiley, 2019. https://doi.org/10.1111/ecog.04444.
[Published Version] View | Files available | DOI | WoS
 

2019 | Journal Article | IST-REx-ID: 6972 | OA
Lenzen, Christoph, and Joel Rybicki. “Self-Stabilising Byzantine Clock Synchronisation Is Almost as Easy as Consensus.” Journal of the ACM. ACM, 2019. https://doi.org/10.1145/3339471.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.” In 2018 IEEE Conference on Decision and Control. IEEE, 2019. https://doi.org/10.1109/cdc.2018.8619625.
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2019 | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.” In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019. https://doi.org/10.1145/3295500.3356222.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Journal Article | IST-REx-ID: 7214 | OA
Aganezov, Sergey, Ilya Zban, Vitalii Aksenov, Nikita Alexeev, and Michael C. Schatz. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.” BMC Bioinformatics. BMC, 2019. https://doi.org/10.1186/s12859-019-3208-4.
[Published Version] View | Files available | DOI | WoS
 

2019 | Conference Paper | IST-REx-ID: 7228
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.” In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.
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2019 | Conference Paper | IST-REx-ID: 7437 | OA
Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
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2019 | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.” In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019. https://doi.org/10.1145/3323165.3323201.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6935 | OA
Foerster, Klaus-Tycho, Janne Korhonen, Joel Rybicki, and Stefan Schmid. “Does Preprocessing Help under Congestion?” In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, 259–61. ACM, 2019. https://doi.org/10.1145/3293611.3331581.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019. https://doi.org/10.1145/3313276.3316407.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6933 | OA
Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.” In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, 74–83. ACM, 2019. https://doi.org/10.1145/3293611.3331633.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Journal Article | IST-REx-ID: 536 | OA
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” Distributed Computing. Springer, 2018. https://doi.org/10.1007/s00446-017-0315-1.
[Published Version] View | Files available | DOI
 

2018 | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.” In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.
[Published Version] View | Files available | DOI
 

2018 | Journal Article | IST-REx-ID: 6001
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.” ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018. https://doi.org/10.1145/3201897.
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2018 | Conference Paper | IST-REx-ID: 7812 | OA
Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
[Published Version] View | Files available | arXiv
 

2018 | Conference Paper | IST-REx-ID: 397
Arbel Raviv, Maya, and Trevor A Brown. “Harnessing Epoch-Based Reclamation for Efficient Range Queries,” 53:14–27. ACM, 2018. https://doi.org/10.1145/3178487.3178489.
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2018 | Journal Article | IST-REx-ID: 43 | OA
Rybicki, Joel, Eva Kisdi, and Jani Anttila. “Model of Bacterial Toxin-Dependent Pathogenesis Explains Infective Dose.” PNAS. National Academy of Sciences, 2018. https://doi.org/10.1073/pnas.1721061115.
[Submitted Version] View | Files available | DOI | WoS
 

2018 | Journal Article | IST-REx-ID: 76 | OA
Lenzen, Christoph, and Joel Rybicki. “Near-Optimal Self-Stabilising Counting and Firing Squads.” Distributed Computing. Springer, 2018. https://doi.org/10.1007/s00446-018-0342-6.
[Published Version] View | Files available | DOI | WoS
 

2018 | Conference Paper | IST-REx-ID: 85 | OA
Gilad, Eran, Trevor A Brown, Mark Oskin, and Yoav Etsion. “Snapshot Based Synchronization: A Fast Replacement for Hand-over-Hand Locking,” 11014:465–79. Springer, 2018. https://doi.org/10.1007/978-3-319-96983-1_33.
[Preprint] View | Files available | DOI | WoS
 

2018 | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018. https://doi.org/10.1145/3212734.3212763.
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2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018. https://doi.org/10.1145/3212734.3212798.
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2018 | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018. https://doi.org/10.1145/3212734.3212756.
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2018 | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018. https://doi.org/10.1145/3210377.3210411.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018. https://doi.org/10.1145/3210377.3210406.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018. https://doi.org/10.1145/3212734.3212785.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 

2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
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2018 | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.” In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018. https://doi.org/10.1137/1.9781611975031.144.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 

2017 | Conference Paper | IST-REx-ID: 487
Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.” In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
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2017 | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 

2017 | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnović. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, 2017.
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 

2017 | Conference Paper | IST-REx-ID: 432 | OA
Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. ML Research Press, 2017.
[Submitted Version] View | Files available
 

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